1. Field of the Invention
The present invention is directed to the field of database browsing and information mining. It is more particularly directed to accessing and browsing database information associated with queries that may mine information associated with a database.
2. Description of the Background Art
A database is a collection of data, organized in the form of tables. A table typically consists of columns that represent attribute types and records that represent specific instances of data associated with the table, and the table has attribute instances associated with the columns. A relational database is a database that may be a set of tables containing information that is manipulated in accordance with the relational model associated with the data. The product marketed under the trademark IBM DB2 stores the data associated with the database in tables, and each table has a name.
On-Line Analytical Processing (OLAP) is a computing technique for summarizing, consolidating, viewing, analyzing, applying formulae to, and synthesizing data according to multiple dimensions. OLAP software enables users, such as analysts, managers, and executives, to gain insight into performance of an enterprise through rapid access to a wide variety of data dimensions that are organized to reflect the multidimensional nature of the enterprise performance data typically by means of hypotheses about possible trends in the data. More particularly, OLAP may be used to analyze corporate data from different viewpoints by identifying interesting associations in the information in a database.
Data mining is a technique employing computer-based techniques to enable users to query structured data stored in computers in forms such as: multidimensional databases, conventional databases, or flat computer files. More particularly, data mining involves extracting computer-based information and enables a user to discover trends about the computer-based information. OLAP is a decision support technique used in data management for the purpose of modeling and analyzing business information, and by means of comparison On-Line Transaction Processing (OLTP) is a technique that may be used to process a computer task immediately upon request, and may employ data from a database. Data mining may be used during OLTP or OLAP processing.
An increasingly popular data model for OLAP applications is the multidimensional database (MDDB). MDDBs are often used by a data analyst for interactive exploration of performance data for finding regions of anomalies in the data that may otherwise be characterized by trends. Problem areas and new opportunities associated with the enterprise are often identified when an anomaly in the enterprise data is located. However, the creation of MDDBs requires computer resources, and it would be useful to be able to efficiently determine relationships and dimensions associated with a database, without relying on a MDDB, when employing OLAP processing techniques.
Each database typically has a set of tables, such as system catalog tables, which are automatically maintained by the computer system and contain information about the tables and other objects that are stored in the database, and about the user of the database and the user's access privileges. Information about the database can be retrieved from the system catalog tables using structured query language (SQL) queries.
SQL is a standardized language for defining and manipulating data in a relational database and may be used during data mining. A query may be an expression whose result is a table. A query searches the records stored in specified tables to find the answer to a question. A query is a request for information from the database based on specific conditions such as, which subset of the data should be retrieved and how the data is to be presented. For example, a request for a list of all departments in a DEPARTMENT table whose budget is greater than $10,000 is an example of a query. It would be useful to understand how database tables and columns, and SQL queries are related to each other when OLAP procedures are employed. Such procedures may analyze, from a hierarchical perspective, data that is above and below the data associated with the query request, and may aggregate the resulting information. Further, the SQL query may analyze the metadata associated with a database. Metadata is information that describes the characteristics of stored data. For instance, data in a database may be described by metadata such as the name of associated database tables and columns.
A browser may be considered a text extender function that enables a user to display text on a computer monitor. Browsing is typically used to examine records in a file, such as a database. By means of example, a browser may operate on one computer, such as a client computer and initiate requests to a second computer, such as a server computer so that information from the second computer may displayed via the first computer. When a user attempts to browse information during OLAP processing, the amount of information may be so large that it is difficult to determine useful dimensions. For example, if a user attempts to browse a database and uses SQL queries it may be difficult to determine how columns, tables, and queries are related to each other.
Given a relational database, it can be difficult to determine useful dimensions when employing OLAP processing. Further, given a set of SQL queries, it can be complicated and time consuming to determine how columns, tables, and queries are related to each other. This is especially true when there are a large number of queries, such as when a user is employing OLAP processing techniques. From the foregoing it will be apparent that there is still a need to improve the determination of a useful set of dimensions and of relationships of database information when employing OLAP processing techniques and to improve the determination of how columns, tables, and queries of a database are related to each other.